KwaiMind is a commercial image editing system that combines general editing capabilities with e-commerce specialization. It uses an agent-based data engine with 1.8 million editing pairs and a multimodal diffusion transformer trained through pre‑training, fine‑tuning, preference optimization, and online reinforcement learning. The system is guided by a vision‑language judge and specialized rewards for click‑through rate, text rendering, and product consistency, and it achieves top scores on ImgEdit, GEdit, REDEdit, and the new Ecom‑Bench, while improving predicted and actual CTR in offline and online experiments.
By Junlong Wu, Zijun Li, Yuting Hu, Jia Sun, Pengcheng Wei, Yimin Zhou, Honglie Wang, Huaiqing Wang, Dewen Fan, Fei Zuo, Haixuan Gao, Lihui Peng, Tingxuan She, Yuqing Li, Boheng Zhang, Fan Yang, Wenwu Ou
arXiv:2609.17475v2 Announce Type: replace
Abstract: Local agents need memory for model execution and working history. We present JustFit, an MLX runtime that coordinates their overlapping allocations...
By Yuhua Chen
arXiv:2609.22818v1 Announce Type: cross
Abstract: Memory-poisoning defenses for LLM agents are typically evaluated by their ability to prevent attacks. However, the traffic they process is rarely adv...
By Pritom Bhowmik
arXiv:2609.24526v2 Announce Type: replace
Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints...
By Foundation Model, Li Auto Inc
arXiv:2609.23953v1 Announce Type: new
Abstract: AI agents that carry a multi-step computer task through on their own became ordinary tools in the past year, and the same autonomy is available to anyo...
By Simiao Ren, Ankit Raj, Tommy Duong, Yuxin Zhang, Dennis Ng, Xingyu Shen, Kidus Zewde, Yuchen Zhou, Neo Tiangratanakul
arXiv:2609.23735v2 Announce Type: new
Abstract: Scientific agents support a range of literature-based research tasks, such as retrieval, question answering, evidence-grounded generation, and claim as...
By ScholarSeed AI Team, Caoqinwei Gong, Xue Jiang, Wei Luo, Xiaoyu Qiu, Jiayi Sheng, Yi Wang, Zheng Yu, Ao Zhang, Haifan Zhang, Hanwei Zhang, Jihai Zhang, Yuan Cao, Wei Chen, Liyun Dai, Wenkai Fang, Guanglei Wang, Kai Ying, Tingyu Zhu, Wotao Yin
arXiv:2609.23790v1 Announce Type: new
Abstract: Every node in a multi-agent large language model (LLM) workflow retrieves context from memory and injects it into its prompt, where those injected toke...
By Vivek Kumar Singh, Preeti Priyam, Gautam Bhowmick
Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes. Scaling their training requires diverse agentic environments, dependable...
Accumulated scientific knowledge advances inquiry when prior findings help researchers choose new questions, design investigations, and interpret results. Realizing this value at scale requires access...
An agent that interacts with users over long periods must recall facts, preferences, events, and changes from a continuously growing interaction history. Existing memory systems often compress interac...
Long-horizon language-model agents accumulate reasoning traces, tool exchanges, and observations whose relevance changes with the current decision. Existing compression strategies often score historic...
SF October 14th: A Birds of a Feather Session on Agentic Engineering
I'm hosting an evening event with Jesse Vincent in San Francisco on Wednesday 14th October for people who are building weird and in...
Sparse communication in decentralized zeroth-order learning requires compatible peer-state coordinates. We characterize this one-hop condition and develop \textsf{ZO-COSMO}, coupling two-query estimat...
The article reports the release of new AI models: Claude Opus 5.5 by Anthropic and GPT‑6 Sol and GPT‑6 Luna by OpenAI, noting that GPT‑6 variants are priced at half the cost of their GPT‑5.6 counterparts. It provides a detailed pricing table comparing input, cached input, and output costs across several models, highlighting how GPT‑6 Luna is among the cheapest ever offered by OpenAI. The author also comments on visual differences in model outputs, noting that GPT‑6 outputs are more muted compared to GPT‑5.6.
Multi-agent debate (MAD) has emerged as a promising paradigm for improving the reasoning accuracy of large language models (LLMs) through iterative peer interaction. Communication topology plays a cen...
Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire them. We ask which of those jobs an agent shoul...
Hindsight relabeling which retroactively replacing a transition's goal with the outcome the agent actually achieved is an effective tool for improving sample-efficiency in Reinforcement Learning (RL)....
Parallel cut research time and cost in half with GPT‑6 Astra. GPT‑6 Astra allowed Parallel’s agents to research and synthesize labor‑market data in half the time and at half the cost versus prior models.
Analog circuit sizing remains a challenging and time-consuming task due to the large design space, strong performance trade-offs, and increasing circuit complexity in scaled technologies. Although rec...
Graphical User Interface (GUI) Agents autonomously interact with software to fulfill user requests, where GUI navigation stands out as the most critical and challenging capability. Mastering this capa...